sinzlab/cGNF
This is the official code for calibration in multi-hypothesis human pose estimation
This project helps researchers and developers working on analyzing human movement to refine their 3D pose estimation models. It takes raw video data or 2D keypoints of human motion as input and outputs a more accurate, calibrated 3D representation of human poses. It's designed for someone specializing in computer vision, biomechanics, or sports science who needs precise, multi-hypothesis 3D human pose data.
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Use this if you are developing or evaluating 3D human pose estimation models and need to ensure their probabilistic outputs are well-calibrated and accurately reflect real-world pose uncertainty.
Not ideal if you are looking for a simple, out-of-the-box solution for general 3D pose tracking in real-time applications without deep dives into model calibration.
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Language
Jupyter Notebook
License
MIT
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Last pushed
May 30, 2023
Commits (30d)
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